Study reveals CS1 student instructor preferences shift over time

A new paper from University of Illinois researchers explores how student preferences for instructors develop and change during a large-scale introductory computer science (CS1) course. The authors, Yiqiu Zhou, Luc Paquette, and Geoffrey Challen, leveraged a novel learning platform that gives students access to instructional content created by multiple instructors. This allowed them to quantify preference emergence and evolution over an entire semester as students repeatedly selected content from different instructors—something traditional one-time course surveys couldn't capture.

The study analyzed both initial and final student preferences, revealing that preference is a dynamic construct continually reshaped by experiences. Surprisingly, student attributes such as prior background did not significantly correlate with initial preferences. However, substantial differences appeared in final preferences across genders and self-reported prior programming experience. The findings suggest that who students prefer to learn from changes as they gain more exposure, and that different student groups may gravitate toward different teaching styles over time. The paper, published in the Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025), offers practical insights for institutions and instructors collaborating on multi-instructor courses, potentially guiding how content is assigned or recommended to maximize engagement and learning outcomes.